Path Planning and Moving Obstacle Avoidance with Neuromorphic Computing

Sakurai, Motoki; Ueno, Yosuke; Masaaki, Kondo

doi:10.1109/isr50024.2021.9419537

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…Therefore, it does not work with completely known and previously mapped environments where no learning is involved, as in ref. [14,24], or those that focus only on path‐planning and, therefore, assume that they can access the status of the entire map, such as in other studies. [ 7,21–23 ]…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it does not work with completely known and previously mapped environments where no learning is involved, as in ref. [14,24], or those that focus only on path-planning and, therefore, assume that they can access the status of the entire map, such as in other studies. [7,[21][22][23] The hippocampus model is responsible for cumulatively storing the result of the local mapping, resulting in a pseudomapping of the environment, while in refs.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed system does not present a global planning algorithm achieved with SNNs like those proposed in ref. [7,[21][22][23][24]. However, these techniques necessarily require knowledge of the entire environment and additional information that is sometimes difficult to obtain.…”

Section: Discussionmentioning

confidence: 99%

“…Other works focus on a specific stage of the whole process, such as environment mapping, [15,[18][19][20] path planning, [7,[21][22][23][24] and motor control. [25,26] In general, these works assume that the previous stages are global and return detailed and complete information that is complex to obtain in a spiking paradigm.…”

Section: Doi: 101002/aisy202300132mentioning

confidence: 99%

“…[7] with neurons representing the points of interest instead of specific positions using a winner take all network, or in ref. [24] for dynamic maps with moving obstacles.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Bioinspired Spike‐Based Hippocampus and Posterior Parietal Cortex Models for Robot Navigation and Environment Pseudomapping

Casanueva-Morato,

Ayuso-Martinez,

Dominguez-Morales

et al. 2023

Advanced Intelligent Systems

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The brain has great capacity for computation and efficient resolution of complex problems, far surpassing modern computers. Neuromorphic engineering seeks to mimic the basic principles of the brain to develop systems capable of achieving such capabilities. In the neuromorphic field, navigation systems are of great interest due to their potential applicability to robotics, although these systems are still a challenge to be solved. This work proposes a spike‐based robotic navigation and environment pseudomapping system formed by a bioinspired hippocampal memory model connected to a posterior parietal cortex (PPC) model. The hippocampus is in charge of maintaining a representation of an environment state map, and the PPC is in charge of local decision‐making. This system is implemented on the SpiNNaker hardware platform using spiking neural networks. A set of real‐time experiments are applied to demonstrate the correct functioning of the system in virtual and physical environments on a robotic platform. The system is able to navigate through the environment to reach a goal position starting from an initial position, avoiding obstacles and mapping the environment. To the best of the authors’ knowledge, this is the first implementation of an environment pseudomapping system with dynamic learning based on a bioinspired hippocampal memory.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Doi: 101002/aisy202300132mentioning

confidence: 99%

“…[7] with neurons representing the points of interest instead of specific positions using a winner take all network, or in ref. [24] for dynamic maps with moving obstacles.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Bioinspired Spike‐Based Hippocampus and Posterior Parietal Cortex Models for Robot Navigation and Environment Pseudomapping

Casanueva-Morato,

Ayuso-Martinez,

Dominguez-Morales

et al. 2023

Advanced Intelligent Systems

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Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview

Casanueva‐Morato,

Ayuso‐Martinez,

Indiveri

et al. 2024

Advanced Intelligent Systems

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The rapid expansion of information systems in all areas of society demands more powerful, efficient, and low‐energy consumption computing systems. Neuromorphic engineering has emerged as a solution that attempts to mimic the brain to incorporate its capabilities to solve complex problems in a computationally and energy‐efficient way in real time. Within neuromorphic computing, building systems to efficiently store the information is still a challenge. Among all the brain regions, the hippocampus stands out as a short‐term memory capable of learning and recalling large amounts of information quickly and efficiently. Herein, a spike‐based bio‐inspired hippocampus sequential memory model is proposed that makes use of the benefits of analog computing and spiking neural networks (SNNs): noise robustness, improved real‐time operation, and energy efficiency. This model is applied to robotic navigation to learn and recall trajectories that lead to a goal position within a known grid environment. The model is implemented on the special‐purpose SNNs mixed‐signal DYNAP‐SE hardware platform. Through extensive experimentation together with an extensive analysis of the model's behavior in the presence of external noise sources, its correct functioning is demonstrated, proving the robustness and consistency of the proposed neuromorphic sequential memory system.

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Path Planning with Improved Dueling DQN Algorithm for UAVs in Unknown Dynamic Environment

Wang,

Zhang,

et al. 2024

Mechanisms and Machine Science

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Path Planning and Moving Obstacle Avoidance with Neuromorphic Computing

Cited by 4 publications

References 9 publications

Bioinspired Spike‐Based Hippocampus and Posterior Parietal Cortex Models for Robot Navigation and Environment Pseudomapping

Bioinspired Spike‐Based Hippocampus and Posterior Parietal Cortex Models for Robot Navigation and Environment Pseudomapping

Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview

Path Planning with Improved Dueling DQN Algorithm for UAVs in Unknown Dynamic Environment

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